In a number of situations, it would be helpful to be able to predict which items can be recommended to users. More particularly, it would be helpful to systematically recommend items to users who have not had an opportunity to sample the items.
For example, with the rapid growth of the Internet, and other distributed computer services, the amount of multimedia content items available to users has expanded enormously. Multimedia content can include news, product information, advertisements, resumes, graphics, photographs, music, games, databases, and so forth. For example, it is estimated that the number of "pages" accessible to users via the worldwide-web (WWW) is measured in tens of millions, and growing every day.
The total collection of items available through the Internet has reached a volume that far exceeds a user's ability to quickly select items that may be of particular interest. There are many known approaches to predicting items that closely match a particular user's interests.
In some prior art preference selection systems, see for example the FIREFLY music selection system at Internet address:
"http://www.firefly.comhtml/about.html,"
users can share music item preferences with each other. The preferences are captured as observation samples. For example, user A likes items 351 and 1024 very much, and user B likes item 351 very much. These observation samples are analyzed by the system to synthesize recommendations, for example, for user B to try item 1024. As the number of samples becomes large, the recommendations can become better. The system allows users to spend time and money on items that are more likely to be of interest, and avoid items that probably have less appeal. Thus, the system brings value to the users as well as to those offering the goods, services, and information.
The problem with most prior art preference selection systems is that the user/item interaction needs to be centralized. Furthermore, the interactions with the system are explicit, and require a substantial amount of user cooperation. For example, most preference selection mechanisms require the tedious preparation of "profiles" of user interests. A profile is a measurement of a subjective reaction to an item by a user. Such mechanisms have limited effectiveness, since they require a relatively high level of user cooperation. Furthermore, this type of profiling is time consuming, and often applied to out-of-date material. Clearly, in a rapidly evolving market place such as the Internet, this type of selection mechanism is unworkable.
Therefore, there is a need for an item selection system which can more accurately predict user tastes for selected unsampled items. In addition, it should be possible to continuously refine the prediction and selection parameters as users interact with the system.